YOLOv8最新改进系列：YOLOv8改进之添加注意力-ContextAggregation，有效涨点！！！

YOLOv8改进：添加注意力-ContextAggregation

一、更改 yaml文件
二、新建ContextAggregation.py
三、更改 tasks.py
详细改进流程和操作，请关注B站博主：AI学术叫叫兽

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论文地址在这

一、更改yaml文件

已完成更改的yaml文件如下所示，更改了两处哈.

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs

# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
  - [-1, 1, GhostConv, [128, 3, 2]]  # 1-P2/4
  - [-1, 3, C2f, [128, True]]
  - [-1, 1, GhostConv, [256, 3, 2]]  # 3-P3/8
  - [-1, 6, C2f, [256, True]]
  - [-1, 1, GhostConv, [512, 3, 2]]  # 5-P4/16
  - [-1, 6, C2f, [512, True]]
  - [-1, 1, GhostConv, [1024, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]]  # 9
#详细改进流程和操作，请关注B站博主：AI学术叫叫兽 
# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 12

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 15 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 12], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 18 (P4/16-medium)
#详细改进流程和操作，请关注B站博主：AI学术叫叫兽
  - [-1, 1,ContextAggregation, [512]]
  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 21 (P5/32-large)
  - [-1, 1,ContextAggregation, [1024]]
  - [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)
#详细改进流程和操作，请关注B站博主：AI学术叫叫兽
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新建 ContextAggregation.py

#详细改进流程和操作，请关注B站博主：AI学术叫叫兽  持续更新哦


import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, caffe2_xavier_init, constant_init
#详细改进流程和操作，请关注B站博主：AI学术叫叫兽 

from mmcv.cnn import ConvModule
#详细改进流程和操作，请关注B站博主：AI学术叫叫兽 
 
class ContextAggregation(nn.Module):
#详细改进流程和操作，请关注B站博主：AI学术叫叫兽 
 
    def __init__(self, in_channels, reduction=1, conv_cfg=None):
        super(ContextAggregation, self).__init__()
        self.in_channels = in_channels
        self.reduction = reduction
        self.inter_channels = max(in_channels // reduction, 1)
 
        conv_params = dict(kernel_size=1, conv_cfg=conv_cfg, act_cfg=None)
 
        self.a = ConvModule(in_channels, 1, **conv_params)
        self.k = ConvModule(in_channels, 1, **conv_params)
        self.v = ConvModule(in_channels, self.inter_channels, **conv_params)
        self.m = ConvModule(self.inter_channels, in_channels, **conv_params)
 
        self.init_weights()
 
    def init_weights(self):
        for m in (self.a, self.k, self.v):
            caffe2_xavier_init(m.conv)
        constant_init(self.m.conv, 0)
 
    def forward(self, x):
        n, c = x.size(0), self.inter_channels
 
        # a: [N, 1, H, W]
        a = self.a(x).sigmoid()
 
        # k: [N, 1, HW, 1]
        k = self.k(x).view(n, 1, -1, 1).softmax(2)
 
        # v: [N, 1, C, HW]
        v = self.v(x).view(n, 1, c, -1)
 
        # y: [N, C, 1, 1]
        y = torch.matmul(v, k).view(n, c, 1, 1)
        y = self.m(y) * a
 
        return x + y
#详细改进流程和操作，请关注B站博主：AI学术叫叫兽 片
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更改 tasks.py

找到tasks.py中的此代码，替换即可，大约在650行左右。

 if m in (Classify, Conv, GGhostRegNet, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
                 BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, RepC3, SEAttention,ContextAggregation):
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(min(c2, max_channels) * width, 8)
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检查修改是否成功

跑通后是这样的哦~

注意！

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